Geometric Problems in Machine Learning and Singularity Theory - Applications to Neural Networks

Explore a 58-minute lecture from the University of North Carolina at Chapel Hill's Professor Richard Rimanyi examining the intersection of machine learning and singularity theory through the lens of Bayesian Learning Theory. Delve into the mathematical foundations of how matrices multiply to zero and their significance in approximating unknown distributions using generated data. Learn about the crucial role of relative entropy function K and its singularities in determining learning efficiency and model training capabilities. Understand the practical applications of singularity invariants in machine learning contexts, with specific focus on computing learning coefficients in Linear Neural Networks. Developed in collaboration with S. P. Lehalleur, this mathematical exploration bridges theoretical concepts with real-world machine learning applications.